TD3-based air metasurface-assisted wireless covert communication method and system in wireless network

Through the TD3 algorithm of deep reinforcement learning, ARIS assisted wireless hidden communication is optimized, which solves the problems of RIS phase shift fixation and complex calculations of traditional algorithms, realizes more efficient security optimization and system security, adapts to complex environments, and improves the concealment and spectrum efficiency of wireless communications.

CN120567360APending Publication Date: 2025-08-29NORTHWEST A & F UNIV
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Patent Information

Application Number
CN202510471899.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-15
Publication Date
2025-08-29

AI Technical Summary

Technical Problem

In the existing ARIS assisted wireless hidden communication systems, RIS phase shift cannot be adjusted to the optimal according to the actual environment. Traditional optimization algorithms are prone to fall into local optimal solutions, have high computational complexity, and do not fully consider the non-perfect CSI of the eavesdropper, resulting in poor system concealment and inaccurate results.

Method used

The deep reinforcement learning method is adopted to construct an ARIS-assisted wireless hidden communication algorithm based on the TD3 algorithm. By modeling the coordinated work of ground base stations, drones and RIS, the active beamforming, passive beamforming and movement trajectory are optimized, and the reward function is designed to maximize the overall system security rate, taking into account boundaries and power constraints.

Benefits of technology

It improves the concealment and spectrum efficiency of the system, reduces the computational complexity, realizes more efficient security protection optimization, adapts to dynamic environmental changes, and ensures the safe transmission of legal vehicle data.

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Abstract

The invention belongs to the technical field of wireless communication, and discloses a TD3-based ARIS auxiliary wireless covert communication method and system in a wireless network, and the method comprises the steps: designing and determining a network scene, an antenna steering vector and a channel model; an optimization target is constructed, and the overall security rate and limiting conditions of the maximized system are explicitly defined; converting the problem into a Markov decision problem according to a scene and constraint conditions, performing state space and action space modeling, and designing a reward function for an optimization target; and constructing an ARIS-assisted wireless covert communication algorithm based on a TD3 algorithm, training the model, and comparing the trained model with a reference algorithm under different parameter settings for performance verification. According to the method, a suboptimal solution for maximizing the overall security rate of the system can be obtained by using a deep reinforcement learning method, the complexity of modeling by using a traditional algorithm is reduced, the optimization efficiency is improved, and a more efficient solution algorithm is provided for ARIS-assisted wireless covert communication.
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